Decisions to develop new reservoir fields and how to efficiently manage production of current fields are of great significance in the petroleum industry. These decisions must be knowledgeable and often made in a timely manner. In situations where capital expenses are high and other risks abundant, carefully analyzing inherent uncertainties of the reservoir field and how they impact the forecasted production becomes a daunting task. This is especially true when coupled with stringent deadlines, or when in challenging environments such as for offshore reservoirs. If business decisions are made based on incomplete or poor characterizations of the reservoir, or if the characterizations of the reservoir are delayed, substantial financial loss can occur due to premature hydrocarbon contract negotiations, incomplete or inaccurate reservoir certifications, poorly negotiated service contracts, non-optimized recovery of the hydrocarbon reserves, or a combination thereof.
Accurately forecasting the performance of a reservoir requires realistic static and dynamic models representative of the reservoir. Static and dynamic models can be generated from a plurality of different workflows, and are generally populated with the available engineering and geological data of the reservoir. Depending on the complexity and location of the reservoir, this data can be limited as costs can become prohibitive.
Static models generally comprise a structural and stratigraphic framework populated with parameters such as sedimentological properties, permeability distributions, porosity distributions, fluid contacts, and fluid saturations. There are many commercially available products for constructing static models, such as Earth Decision Suite (powered by GOCAD™) distributed by Paradigm Geotechnology BV headquartered in Amsterdam, The Netherlands and Petrel™ from Schlumberger Limited headquartered in Houston, Tex. Static models are typically constructed by a team including geologists, geophysicists, and stratigraphers using reservoir data from a variety of sources such as core samples, well logs, and seismic surveys. Depending on the data interpretation and chosen modeling package, multiple realizations of the same geology may be made, leading to similar, yet different geological models being shaped by quasi-random variations. A certain amount of validation that the static model is an appropriate geological interpretation can be given with additional geochemical and geostatistical analysis; however, only a certain amount of deterministic information may be extracted from the subterranean formation, and one typically relies on applying probabilistic methods in combination with the obtained data to construct a reasonable static reservoir model.
Dynamic models typically comprise upscaled versions of static models populated with additional information such as reservoir fluid flow rates and reservoir pressure-volume-temperature (PVT) characteristics. The upscaling process entails coarsening the fine-scale resolution of the static model to allow for computational tractability. There are also many commercially available products for building these dynamic models, such as Chevron's proprietary CHEARS™ simulation-package or Schiumberger's ECLIPSE™ reservoir simulator. Dynamic models are normally constructed by reservoir engineers, for which a different skill profile is typically demanded in comparison to the assemblers of the static model.
Due to the multi-disciplinary roles needed to construct static and dynamic models of a subterranean reservoir, certain significant intricacies of the reservoir's geology can be occasionally surrendered as the importance of these aspects are overlooked or simply undervalued.
Once production of the reservoir begins, it is desired that the static and dynamic models be continuously updated with new production data so that they may reliably predict future extraction amounts. This is another challenge, as the real time data should be managed and filtered to best use it effectively to delineate the reservoir model description and flow parameters. Determining what production data is useful and then manually inputting this data into the model is often tedious and very time consuming. Typically, the static model needs to first be conditioned with the filtered production data, and reconstructed creating a static offspring model. The static parameters of this new offspring model are then applied to the dynamic model so that it can be simulated, thus producing an updated dynamic model. This process is iteratively repeated, typically in a linear fashion, to eventually generate an updated reservoir production forecast. One becomes limited in the number of model iterations they can run due to the time and cost constraints needed to perform a proper optimization investigation. The performance predictions may also not be “optimized” due to poor analysis of the many inherent uncertainties of the static and dynamic models. Additionally, loss of data or its significance may be overlooked during transfer of data between the reservoir production engineers, the static modeling team, and the dynamic modeling team.
The updated reservoir forecast is used to sufficiently understand the complex chemical, physical and fluid flow processes occurring in the reservoir to predict future reservoir behavior and maximize the ultimate hydrocarbon recovery. To test the reliability of the forecast, history matching techniques can be employed. These techniques attempt to find plausible flow solutions by trying to mimic past performances through simulation of the reservoir model and comparing the outcome with actual production data. Mathematically, this is done by searching for minima of an objective function below a predetermined threshold value. In certain circumstances, a good match may not be found and one must rely on the best apparent match produced. In other instances, a plurality of acceptable history matching solutions may be found leading to inconclusive predictions.